abmlog

This module's name is an acronym for: All possible Boolean Models Link Operator Generator.

The only required input is a single interactions file (.sif) which describes the network topology. Using that file, we build a boolean model whose equations are based on the following standardized format:

A *= ( B or C or ... ) and not ( D or E or ... )

, translating thus lines from the .sif file like e.g. B -> A, C -> A (B,C are positive regulators of A - activators) and D -| A, E -| A (D,E are negative regulators of A - inhibitors).

abmlog generates every possible boolean model out of the initial one, by changing the link operator (and not vs or not) between the positive and negative regulators in every possible way for all equations. Thus the full range of link operator-parameterized boolean models is produced, from the one having all link operator equations with and not to the one having them with or not.

For models that have a large number of equations with a link operator, making thus the generation of all possible link operator models infeasible/untractable, we provide a random model generator.

Install

First, install gitsbe. Then:

git clone https://github.com/druglogics/abmlog.git
cd abmlog
mvn clean install

The above command creates a package <name>-jar-with-dependencies.jar file with all dependencies installed, in the target directory.

Alternatively, you could just use directly one of the released packages.

Examples

We now provide some examples using the BooleanModelGenerator and RandomBooleanModelGenerator.

Get the list of all provided user options:

java -cp target/abmlog-1.6.0-jar-with-dependencies.jar eu.druglogics.abmlog.BooleanModelGenerator

The example below will generate all the possible boolean models and export them with no calculation of attractors:

java -cp target/abmlog-1.6.0-jar-with-dependencies.jar eu.druglogics.abmlog.BooleanModelGenerator --file=test/test.sif

The next example will generate the models and also calculate the fixpoints (stable states):

java -cp target/abmlog-1.6.0-jar-with-dependencies.jar eu.druglogics.abmlog.BooleanModelGenerator --file=test/test.sif --attractors=fixpoints

The examples above use only 1 core to generate the models, but this job can be parallelized by giving an extra parameter --parallel (so that all available cores are used):

java -cp target/abmlog-1.6.0-jar-with-dependencies.jar eu.druglogics.abmlog.BooleanModelGenerator --file=test/test.sif --attractors=fixpoints --parallel

The example below will generate the models, calculate the minimal trapspaces and put them into directories with maximum 5 models/directory:

java -cp target/abmlog-1.6.0-jar-with-dependencies.jar eu.druglogics.abmlog.BooleanModelGenerator --file=test/test.sif --attractors=trapspaces --max-dir-size=5

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For a boolean model that has e.g. 23 boolean equations with a link operator, generating all 2^23 possible link operator permutated models might be very challenging task (time-wise, space-wise, etc.). Another case is that we may just want a sample out of the pool of all possible models. These use cases are covered by a simple random boolean model generator that produces structurally different models based on link operator mutations.

Generating 100 models from the input network (attractors are calculated and are always the fixpoints):

java -cp target/abmlog-1.6.0-jar-with-dependencies.jar eu.druglogics.abmlog.RandomBooleanModelGenerator --file=test/network.sif --num=100

All attractors are calculated using the BioLQM library. The result models are saved in both BoolNet (.bnet) and Gitsbe (.gitsbe) formats.

Output

For both model generators, the output consists of a results_<network_file>_<date> directory which holds the log file(s) and a models directory where the models are saved.

In the case of the BooleanModelGenerator, we split the models directory to several if the amount of models exceeds the value of the specified max-dir-size (default: 100000) thus avoiding filesystem issues that may arise. For large models, always try to use a machine with as many cores as possible (and the --parallel option of course) as well as check that the number of inodes (for Linux systems) is enough to store the total amount of models that will be generated (this information is outputed on the first lines of the main log file).